Remote sensing of night-time light

Li, Xi; Elvidge, Christopher D.; Zhou, Yuyu; Cao, Changyong; Warner, Timothy A.

doi:10.1080/01431161.2017.1351784

Cited by 55 publications

(42 citation statements)

References 15 publications

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“…In recent years, the technology and application of remote sensing of nighttime light have attracted increasingly extensive attention [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Nighttime light (NTL) imagery acquired by remote sensing technology intuitively exhibit the distributions of artificial nocturnal radiances, which is an increasingly useful indicator in investigating socioeconomic activities of human being [4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm 2 ·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.(VIIRS DNB) onboard the Suomi National Polar Partnership (SNPP) satellite, were often used for remote sensing-based estimation of EPC [3,5,6].DMSP/OLS data has been widely used due to its long temporal coverage, from 1992 to 2013, which was convenient for social and economic research of long time series [18,23]. Compared with DMSP/OLS data, the VIIRS DNB data was superior with higher spatial resolution (15 arc-second vs. 30 arc-second of DMSP/OLS), shorter temporal intervals (monthly vs. annual of DMSP/OLS) and wider radiometric detection range (free from saturation problem), which usually produced more reliable research results [5,6].…”

mentioning

confidence: 99%

“…(VIIRS DNB) onboard the Suomi National Polar Partnership (SNPP) satellite, were often used for remote sensing-based estimation of EPC [3,5,6].…”

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confidence: 99%

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Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Lin

Shi

2020

IJGI

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The nighttime light (NTL) imagery acquired from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) enables feasibility of investigating socioeconomic activities at monthly scale, compared with annual study using nighttime light data acquired from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS). This paper is the first attempt to discuss the quantitative correlation between monthly composite VIIRS DNB NTL data and monthly statistical data of electric power consumption (EPC), using 14 provinces of southern China as study area. Two types of regressions (linear regression and polynomial regression) and nine kinds of NTL with different treatments are employed and compared in experiments. The study demonstrates that: (1) polynomial regressions acquire higher reliability, whose average R square is 0.8816, compared with linear regressions, whose average R square is 0.8727; (2) regressions between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC steadily exhibit the strongest reliability among the nine kinds of processed NTL data. In addition, the polynomial regressions for 12 months between denoised NTL with threshold of 0.3 nW/(cm2·sr) and EPC are constructed, whose average values of R square and mean absolute relative error are 0.8906 and 16.02%, respectively. These established optimal regression equations can be used to accurately estimate monthly EPC of each province, produce thematic maps of EPC, and analyze their spatial distribution characteristics.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Lin

Shi

2020

IJGI

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“…In addition, compared to ordinary remote sensing images, nighttime light remote sensing images can more accurately reflect human activities, and a large number of studies have shown that nighttime light is highly correlated with Gross Domestic Product(GDP). Therefore, nighttime light can also be used to characterize the level of socioeconomic development in a region [22][23][24]. However, it can be seen from the relevant literature that most nighttime light remote sensing data have been used to separately study urban expansion [25][26][27] or socioeconomic phenomena [24,[28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Exploring Urban Expansion and Socioeconomic Vitality Using NPP-VIIRS Data in Xia-Zhang-Quan, China

2019

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Studying the spatiotemporal evolution of urban expansion in the Xia-Zhang-Quan metropolitan area (XZQ) is of crucial importance, to effectively guide coordinated development and industrial adjustment during urbanization. Based on National Polar Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data this study used an analytical method of irregular spatial grids to explore and evaluate the dynamics of urban spatial expansion and urban socioeconomic vitality in XZQ between 2013 and 2017. The results show that the gulf-type urban development strategy of Xiamen has played a key role in the integration and development of XZQ. The urban area increased by 20–30% and increased intensity of socioeconomic activities was demonstrated by observed increases in the total brightness of urban nighttime light. Due to its unique advantages in the agricultural industries, Zhangzhou made significant economic progress during 2013–2017. At the same time, driven by spatial processes in Xiamen, areas such as the Zhangzhou Port Area and Longhai also achieved significant progress. This is also the case in Quanzhou and thus, collectively there is evidence of multi-polar growth. Jinjiang and Shishi effectively utilized coastal port economy development advantages to play a key role in the integration and development of XZQ. There were both commonalities and differences in terms of the characteristics of spatial expansion in different cities of XZQ. In summary, this study provides evidence to support further promotion of coordinated development in XZQ, and with appropriate caveats these findings could also be transferred to other urban agglomerations.

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“…It has been extensively used in understanding the various anthropogenic phenomenon. Also, correlating the NTL brightness with other variables such as buildings and settlement areas, bridges and communication routes, and demographic and socio-economic activities have generated good results which have proved useful for decision making [65,66,82,83].…”

Section: Nighttime Lightmentioning

confidence: 99%

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

et al. 2019

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Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

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Remote sensing of night-time light

Cited by 55 publications

References 15 publications

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Statistical Correlation between Monthly Electric Power Consumption and VIIRS Nighttime Light

Exploring Urban Expansion and Socioeconomic Vitality Using NPP-VIIRS Data in Xia-Zhang-Quan, China

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

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